Citations

Abstract Background There is growing evidence that low-density lipoprotein-triglyceride (LDL-TG) may be a better biomarker to define atherosclerotic cardiovascular disease (ASCVD) risk than LDL-cholesterol (LDL-C), which is currently used to stratify risk and as a treatment goal. Because LDL-TG typically requires additional advanced lipid testing, we developed an equation for estimating LDL-TG (eLDL-TG) from the results of the standard lipid panel. Methods Results from a general dyslipidemic population (N = 40 202), which were obtained with beta-quantification (BQ) reference method, were randomly divided into a training and validation datasets and used to develop the equation for estimating LDL-TG shown in Fig. 1A. Association of Sampson-NIH-LDL-C (NIH-LDL-C) and eLDL-TG with ASCVD risk markers was done in the National Heart and Nutrition Examination Survey (NHANES) (N = 37 053) and with ASCVD events in the Atherosclerosis Risk in Communities (ARIC) study (N = 14 749). Results The comparison between eLDL-TG and measured LDL-TG was only modest (R2 = 0.601, slope = 0.611), but eLDL-TG (Fig. 1B) showed better risk stratification than NIH-LDL-C (Fig. 1C) by survival curve analysis in ARIC. Moreover, receiver-operating characteristic (ROC) analysis showed that eLDL-TG was better than all other lipid-marker tests for ASCVD events (AUC: eLDL-TG = 0.633, HDL-C = 0.627, sdLDL-C = 0.621, remnant-cholesterol = 0.613, NonHDL-C = 0.613, TG = 0.603, apoB = 0.598, NIH-LDL-C = 0.590, TC = 0.568). In NHANES, eLDL-TG was more strongly associated with hypertriglyceridemia, obesity, diabetes, metabolic syndrome and increased high-sensitivity C-reactive protein than NIH-LDL-C. Inclusion of eLDL-TG in a Cox-Proportional Hazards Model along with other conventional risk factors (age, sex, race, diabetes, systolic blood pressure, smoking, HDL-C, and total cholesterol) did not further improve risk prediction. Conclusions Like LDL-C, LDL-TG can also be estimated from the results of the standard lipid panel. It is a better univariate risk marker than LDL-C and hence can be useful for initial ASCVD risk stratification.

Abstract Background Low-density lipoprotein cholesterol (LDL-C) is used to assess atherosclerotic cardiovascular disease (ASCVD) risk and to manage lipid-lowering therapy. Proprotein convertase subtilisin/kexin (type 9) serine protease (PCSK9) inhibitors decrease LDL-C by up to 70% when used in conjunction with statins. Due to cost, insurance companies restrict their use. To be eligible for PCSK9 therapy, a patient must have pre-existing ASCVD and LDL-C > 70 mg/dL, while on a maximally tolerated dose of statins. Most clinical laboratories calculate LDL-C by the Friedewald Equation (FWLDL-C), which utilizes results from the standard lipid panel (total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C)). However, this often leads to an underestimation of LDL-C, especially when TG concentrations are elevated. More accurate equations have recently been developed, such as the Martin (MLDL-C), Sampson-NIH (SLDL-C) and extended Martin (ext-MLDL-C) equations. Using LDL-C determined by the beta-quantification reference method (BQ), we developed the following enhanced version of the Sampson-NIH Equation (eSLDL-C), which includes apoB as an independent variable: eSLDL-C = TC/1.15-(HDL-C)/1.25-TG/6.98-(TG × NonHDL-C)/1115 + TG2/8903.23 + (TG × ApoB)/1237 + ApoB/4.54–4.73 Methods Results for clinically ordered ApoB, BQ, and lipid panels were used. To test the accuracy of the various equations, we compared BQLDL-C to FWLDL-C, MLDL-C, extMLDL-C, SLDL-C and eSLDL-C by regression analysis (N = 12 101, TG < 800). Accuracy of each equation was compared to the BQ reference method for classifying patients (LDL-C ≤ 150, n = 9374) as either being below or above the 70 mg/dL treatment decision threshold for PCSK9 therapy. Results The eSLDL-C equation performed substantially better than the other equations for estimating low LDL-C (BQLDL-C ≤ 100 mg/dL, TG 5–800 mg/dL, n = 4115), with a mean absolute difference (MAD) of 3.80 mg/dL (compared to SLDL-C: 6.05; FWLDL-C: 8.71; MLDL-C: 6.43; extMLDL-C: 6.39). It also had the best overall normalized Matthew’s Correlation Coefficient (nMCC) for the best balance of sensitivity and specificity for identifying patients that are above the 70 mg/dL treatment threshold (eSLDL-C-94.7% (92.2% spec, 98.5% sen), SLDL-C-90.7% (94.8% spec, 95.5% sen), FWLDL-C-88.2% (98.0% spec, 92.7% sen), MLDL-C-90.3% (88.9% spec, 96.5% sen), and extMLDL-C-90.1% (88.4% spec, 96.5% sen)). In our dataset with 1125 subjects being below the 70 mg/dL threshold by BQLDL-C, FWLDL-C misclassified an additional 601 subjects below this threshold (Negative Predictive Value 65%). MLDL-C and extMLDL-C misclassified 289 subjects falsely low, whereas eSLDL-C only has 126 subjects falsely classified lower than the 70 mg/dL threshold. Conclusion We show that the enhanced Sampson-NIH equation, which includes apoB, estimates low LDL-C more accurately than other equations. The use of the new equation would improve access to those patients who might benefit from PCSK9 therapy but have falsely low LDL-C by the FWLDL-C equation or other LDL-C equations. The new equation is also more specific than the MLDL-C and extMLDL-C equations and would, therefore, also reduce the overutilization of PCSK9 therapy by these particular equations for those patients who do not require it, according to the reference BQ method and current guidelines.

An accurate estimate of glomerular filtration rate (eGFR) is essential for proper clinical management, especially in patients with kidney dysfunction. This prospective observational study evaluated the real-world performance of the nuclear magnetic resonance (NMR)-based GFRNMR equation, which combines creatinine, cystatin C, valine, and myo-inositol with age and sex. We compared GFRNMR performance to that of the 2021 CKD-EPI creatinine and creatinine-cystatin C equations (CKD-EPI2021Cr and CKD-EPI2021CrCys), using 115 fresh routine samples of patients scheduled for urinary iothalamate clearance measurement (mGFR). Median bias to mGFR of the three eGFR equations was comparably low, ranging from 0.4 to 2.0 mL/min/1.73 m2. GFRNMR outperformed the 2021 CKD-EPI equations in terms of precision (interquartile range to mGFR of 10.5 vs. 17.9 mL/min/1.73 m2 for GFRNMR vs. CKD-EPI2021CrCys; p = 0.01) and accuracy (P15, P20, and P30 of 66.1% vs. 48.7% [p = 0.007], 80.0% vs. 60.0% [p < 0.001] and 95.7% vs. 86.1% [p = 0.006], respectively, for GFRNMR vs. CKD-EPI2021CrCys). Clinical parameters such as etiology, comorbidities, or medications did not significantly alter the performance of the three eGFR equations. Altogether, this study confirmed the utility of GFRNMR for accurate GFR estimation, and its potential value in routine clinical practice for improved medical care.

New more effective lipid-lowering therapies have made it important to accurately determine Low-density lipoprotein-cholesterol (LDL-C) at both high and low levels. LDL-C was measured by the β-quantification reference method (BQ) (N = 40,346) and compared to Friedewald (F-LDL-C), Martin (M-LDL-C), extended Martin (eM-LDL-C) and Sampson (S-LDL-C) equations by regression analysis, error-grid analysis, and concordance with the BQ method for classification into different LDL-C treatment intervals. For triglycerides (TG) < 175 mg/dL, the four LDL-C equations yielded similarly accurate results, but for TG between 175 and 800 mg/dL, the S-LDL-C equation when compared to the BQ method had a lower mean absolute difference (mg/dL) (MAD = 10.66) than F-LDL-C (MAD = 13.09), M-LDL-C (MAD = 13.16) or eM-LDL-C (MAD = 12.70) equations. By error-grid analysis, the S-LDL-C equation for TG > 400 mg/dL not only had the least analytical errors but also the lowest frequency of clinically relevant errors at the low (<70 mg/dL) and high (>190 mg/dL) LDL-C cut-points (S-LDL-C: 13.5%, F-LDL-C: 23.0%, M-LDL-C: 20.5%) and eM-LDL-C: 20.0%) equations. The S-LDL-C equation also had the best overall concordance to the BQ reference method for classifying patients into different LDL-C treatment intervals. The S-LDL-C equation is both more analytically accurate than alternative equations and results in less clinically relevant errors at high and low LDL-C levels.

Abstract Cholestatic liver diseases, such as primary sclerosing cholangitis and primary biliary cirrhosis, can lead to serum accumulation of lipoprotein X (LpX). LpX is a multilamellar particle high in cholesterol but lacking structural apolipoproteins A1 or B. The absence of ApoB results in no negative feedback on cholesterol biosynthesis and prevents LpX clearance from the liver. While clinical signs and symptoms typically precede laboratory findings, it is possible that in medically complex patients the identification of LpX could be the first observation of cholestatic liver disease. Traditional laboratory methods are insufficient to identify LpX as it is of similar density to low-density lipoprotein (LDL). LpX contains a high concentration of cholesterol which is erroneously reported as LDL-C by routine clinical methods. As LpX is a rare complication of liver disease, clinicians may presume the elevation is a coincidental familial hypercholesterolemia rather than a sequela of liver disease. Currently, lipoprotein gel electrophoresis is the only laboratory method to identify LpX. In this method only performed in specialty lipid laboratories, LpX is readily identified by its unique reverse electrophoretic mobility relative to other lipoproteins. The objective of this study was to characterize lipid panels from LpX-positive samples and develop a suitable mechanism to identify LpX-containing samples with good clinical validity. From 21,377 clinical electrophoresis results reported between Nov 2011 to Nov 2021, LpX was identified in 157 serum samples. Overall, patients with LpX were younger (median 44y vs. 55y, p&lt;0.0001) with significantly higher total cholesterol (812mg/dL vs 190mg/dL, p&lt;0.0001) and lower high density lipoprotein-cholesterol (HDL-C; 3mg/dL vs 45mg/dL, p&lt;0.0001). Data were randomly split (70/30) into training (n=14,964) and testing (n=6,413) cohorts. Receiver operator characteristic curve analysis identified optimal thresholds of 22.5 mg/dL HDL-C (AUC = 0.94) and 378.5 mg/dL total cholesterol (AUC = 0.91), as well as a nonHDL-C/HDL-C ratio of 9.2 (AUC = 0.995). Applying these cutoffs to the testing cohort achieved a sensitivity/specificity of 98%/81% for HDL-C, 96%/87% for total cholesterol, and 98%/94% for nonHDL-C/HDL-C ratio. A multivariate model combining these three parameters showed a sensitivity/specificity of 97%/85%, respectively. In conclusion, low HDL-C, elevated total cholesterol and a ratio of nonHDL-C/HDL-C &gt;9.2 are associated with the presence of LpX. The ratio of nonHDL-C/HDL-C is the most sensitive and specific predictor of LpX. If confirmed in other cohorts, laboratories could include a reporting comment on lipid panels with a nonHDL-C/HDL-C ratio &gt;9.2 cautioning a high suspicion for the presence of LpX and recommending confirmatory testing. It may also be prudent to caution that LDL-C results may not be accurate. In conclusion, identifying patients with high suspicion of LpX based on abnormal lipid panel results may aide in clinical diagnosis, even when an assay to detect LpX is not readily available.

Abstract Background: Altered hemodynamics in liver disease often results in overestimation of glomerular filtration rate (GFR) by creatinine-based GFR estimating (eGFR) equations. Recently, we have validated a novel eGFR equation based on serum myo-inositol, valine, and creatinine quantified by nuclear magnetic resonance spectroscopy in combination with cystatin C, age and sex (GFRNMR). We hypothesized that GFRNMRcould improve chronic kidney disease (CKD) classification in the setting of liver disease. Results: We conducted a retrospective multicenter study in 205 patients with chronic liver disease (CLD), comparing the performance of GFRNMRto that of validated CKD-EPI eGFR equations, including eGFRcr (based on creatinine) and eGFRcr-cys (based on both creatinine and cystatin C), using measured GFR as reference standard. GFRNMR outperformed all other equations with a low overall median bias (-1 vs. -6 to 4 ml/min/1.73 m2 for the other equations; p < 0.05) and the lowest difference in bias between reduced and preserved liver function (-3 vs. -16 to -8 ml/min/1.73 m2for other equations). Concordant classification by CKD stage was highest for GFRNMR (59% vs. 48% to 53%) and less biased in estimating CKD severity compared to the other equations. GFRNMR P30 accuracy (83%) was higher than that of eGFRcr (75%; p = 0.019) and comparable to that of eGFRcr-cys (86%; p = 0.578). Conclusions: Addition of myo-inositol and valine to creatinine and cystatin C in GFRNMR further improved GFR estimation in CLD patients and accurately stratified liver disease patients into CKD stages.

Abstract Background Measurement of cholesterol within lipoprotein subfractions may aid in cardiovascular disease prediction. Simple, homogenous enzymatic assays for the direct measurement of lipoprotein subfractions have been developed to measure small dense low-density lipoprotein cholesterol (sdLDL-C), high-density lipoprotein-3 cholesterol (HDL3-C), and triglyceride-rich lipoprotein (TRL-C) cholesterol. The objective of this study was to determine biological variability for sdLDL-C, HDL3-C, and TRL-C in a healthy reference population to facilitate interpretation of these analytes. Methods Serum samples were collected from 24 healthy subjects (n = 14 female/10 male) daily for 3 days while non-fasting, and daily for 5 days, weekly for 4 weeks, and monthly for 6 months after overnight fasting. sdLDL-C, HDL3-C, and TRL-C cholesterol were measured by homogenous enzymatic assays. Sources of variability (between-subject, within-subject, and analytical) were calculated using random-effects regression models. Reference change value (RCV) and index of individuality (II) for each time period were determined from the variance components. Results Analytic variability (daily, weekly, and monthly CVA) was &lt;3% for each analyte. Monthly within-subject variability (CVI) was 17.1% for sdLDL-C, 7.4% for HDL3-C, and 25.7% for TRL-C. Most of the monthly variation was attributed to between-subject variation for all 3 analytes. Overall RCVs for monthly measurements were 18.1 mg/dL for sdLDL-C, 6.1 mg/dL for HDL3-C, and 16.0 mg/dL for TRL-C. IIs were &lt;0.6 for sdLDL-C and HDL3-C, and 0.81 for TRL-C. Conclusions sdLDL-C, HDL3-C, and TRL-C showed moderate within-subject variability, but high between-subject variability, in a healthy reference population. Given the high individuality of each analyte, population-based reference intervals may be inadequate to detect clinically significant changes.

Accurate and precise monitoring of kidney function is critical for a timely and reliable diagnosis of chronic kidney disease (CKD). The determination of kidney function usually involves the estimation of the glomerular filtration rate (eGFR). We recently reported the clinical performance of a new eGFR equation (GFRNMR) based on the nuclear magnetic resonance (NMR) measurement of serum myo-inositol, valine, and creatinine, in addition to the immunoturbidometric quantification of serum cystatin C, age and sex. We now describe the analytical performance evaluation of GFRNMR according to the Clinical and Laboratory Standards Institute guidelines. Within-laboratory coefficients of variation (CV%) of the GFRNMR equation did not exceed 4.3%, with a maximum CV% for repeatability of 3.7%. Between-site reproducibility (three sites) demonstrated a maximum CV% of 5.9%. GFRNMR stability was demonstrated for sera stored for up to 8 days at 2–10°C and for NMR samples stored for up to 10 days in the NMR device at 6 ± 2°C. Substance interference was limited to 4/40 (10.0%) of the investigated substances, resulting in an underestimated GFRNMR (for glucose and metformin) or a loss of results (for naproxen and ribavirin) for concentrations twice as high as usual clinical doses. The analytical performances of GFRNMR, combined with its previously reported clinical performance, support the potential integration of this NMR method into clinical practice.